Today's Deep-Dive: open-appsec

Welcome to the deep dive your shortcut to getting up to speed quickly today. Our

mission is really focused

We're tackling one of the biggest headaches in digital security keeping web apps

and API safe

We are diving deep into the sources around open app sec

It's a machine learning engine and the promise here is pretty big fundamentally

changing web defense moving security away from you know

Reactive fixes towards automatic preemptive protection against the big stuff like

the OWASP top 10 and yeah

Even zero days now before we jump right in just a quick word this deep dive is

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Okay, let's get into it if you're in tech

You probably know the the pain of the traditional web application firewall the way

up that sits in front of your app in historically

Managing it it's a constant battle hours writing signatures exceptions dealing with

false positives. Yeah, that's exactly the context

The source material really positions open app sec as well the opposite of that high-maintenance

reality

They define it pretty simply a machine learning security engine one that

automatically and crucially preemptively

Stops threats against web apps and apis

The core idea isn't just catching known bad stuff

It's about learning what normal looks like for your specific application and that's

the bit that really grabs your attention, right?

Painless to configure and manage effective security and it's open source honestly

knowing the usual operational grind that almost sounds well

Too good to be true. Well, the claim really rests on two core ideas. They hammer

home

preemptive and precise

Preemptive means it acts without needing those constant signature updates. This is

where those pretty startling claims come in

They state it blocked major zero days like log4 shell and spring4 shell with no

prior knowledge. No updates needed

That's well, that's a huge deal. Wow. Okay, if that holds water, it means the

system isn't just matching patterns

It's understanding the attacks intent somehow precisely. Yeah, and that links

directly to the second idea

precision

Because the machine learning is continuously adapting to your environment. It

supposedly cuts down

Drastically on the noise, you know the false positives the endless exceptions that

make old-school wafts such a chore

Okay. So before we get into the ML engine itself the sort of brain of the operation

what happens first when a request comes in?

What's the groundwork? Right? It needs to fully understand the data first which can

be tricky

So for every single HTTP request the engine starts by decoding everything all the

parts of the payload

It pulls out any nested data specifically looking for JSON or XML sections hidden

inside

Only after it's fully parsed and understood the raw request and applied basic IP

checks

Then the machine learning part kicks in. Got it. So let's unpack that core

intelligence

The sources talk about a two-phase dual engine process not just one big AI model.

How does it figure out good versus bad?

Yeah, it's clever. It combines

Global knowledge with very specific local context. The main goal is spotting

requests that just don't fit the normal pattern

Things that fall outside how users should be interacting with the application. Okay

phase one

That's the supervised model the global schooling part. That's a great way to think

about it

Yeah, the supervised model is like the global expert

It's trained offline on a massive data set millions of requests both malicious

attack traffic and perfectly normal benign traffic

collected from all over

Think of it as the system's baseline understanding of known attack types seen

across the Internet

What kind of data does it look at it considers a whole range of things?

Known attack indicators the IP's reputation user agents browser fingerprints other

sort of contextual clues

It does a quick comparison does this incoming request look like any known global

attack patterns

The source has mentioned there's a basic model for say testing or monitoring only

but the advanced model

The one they recommend for production gets updated via their portal. It keeps

learning globally

Okay, so the supervised model acts as a first filter

Let's the obviously good stuff through flags that clearly bad based on global

patterns. But what about the gray area stuff?

That looks suspicious globally, but might be okay locally. That's where phase two

comes in. Exactly

That's the handoff if phase one says hmm

This looks risky or suspicious then the analysis moves to the unsupervised model

and this one is completely different

It's the local detective. It doesn't use that global training data instead

It builds its intelligence in real time right there in your protected environment.

Ah, okay

So it's learning the specific quirks and patterns of my application my users

I see it looks at hyper local context things like the exact URL being hit the usual

traffic patterns for that specific endpoint

Maybe the history of the user involved it uses this local perspective to generate a

final confidence score and that score decides

Block or allow it's that blend the global knowledge of attacks plus the specific

understanding of your applications behavior

That's supposed to deliver that promised precision interesting

So moving beyond just the core ML engine the sources indicate this isn't just a

simple filter even with the two phases

What other security layers are built in? It sounds like a sweep. It really is

presented as a comprehensive stack

Yeah bundled into one engine a big one for modern setups is API security

The engine apparently discovers your API is automatically and helps narrow the

attack surface

It can enforce strict open API schema validation making sure the API calls actually

look like they're supposed to

Exactly making sure the traffic fits the expected format and what about protection

against, you know standard known vulnerabilities

We still need that right? Absolutely, and that's integrated too. There's an

enhanced intrusion prevention system and IPS this protects against

I think the number was over 2,800

specific web CVEs known vulnerabilities

Uses NSS certified tech and an open snort 3.0 engine. So you get this smart ML

detection and strong defense against known exploits

Best of both worlds supposedly. Okay makes sense. What else bots are a huge problem.

Yep covered. They include anti-bot capabilities

designed to identify and stop automated attack scrapers

Intrusion attempts before they cause damage right that handles incoming traffic

threats

But what about uploads malicious files are a classic vector. Good point. They've

built in file security

It scans uploaded files automatically and it checks them against a big cloud

repository for known malicious file reputations

Helps stop nasty executables or scripts getting under your servers. Mmm. I was I

mentioned of some pretty

Advanced rate limiting controls more than just blocking IPs. Yeah, the rate

limiting seems quite flexible

You can set limits based not just on IP address

But on identifiers inside the session like, you know specific keys found in GWT's

JSON web tokens or maybe values and cookies or custom headers

That's really useful for protecting individual user accounts or API keys from brute

force or abuse. That is more granular

Definitely and one more interesting feature crowd wisdom. They partner with crowd

sec

This means the system gets real-time threat intelligence from I think it's over 64,000

contributing servers

So if an IP address starts attacking applications elsewhere on the network

Your system can learn about it almost instantly and block it proactively kind of a

neighborhood watch for servers

Okay, let's circle back to those zero-day claims because that's really the headline

grabber blocking log4 shell without a signature

How do the sources justify that confidence? It still sounds almost magical the

justification comes back to that unsupervised model the anomaly detection part

Because it learns normal so well anything drastically outside that norm gets flagged

Even if the specific intact method has never been seen before

The sources specifically list log4 shell spring4 shell also text4 show that Apache

text vulnerability CVE

2022 port 2 8 8 9 and they even mentioned a tricky wife bypass technique using JSON

syntax hidden in SQL injection payloads

In each case the argument is the system saw behavior or request structures that

were just weird

Highly anomalous compared to the learned baseline for that application. So it got

blocked regardless of the specific attack signature, right?

It's not matching a known bad pattern. It's spotting not normal. Okay, so thinking

about implementation if I'm running modern infrastructure cloud

Containers CICD pipelines. How does this fit in if the goal is painless it can

involve tearing everything down?

No, it seems designed explicitly for that. It's described as cloud native and CICD

friendly

You can deploy it using declarative methods like infrastructure as code or manage

it via API's

Platform wise it usually works as an add-on

You can deploy it with Linux Docker Kubernetes setups and it integrates with common

reverse proxies NG INX Kong API six

envoy

The usual suspects and management flexibility different teams have different

preferences seems like it

You can use declarative config files Kubernetes helm charts or annotations for that

automated k8s workflow

Or they offer a SAS web interface for managing it visually so options depending on

your operational style

Good good. And finally that open source aspect. That's pretty important for trust,

especially with security tools. Absolutely crucial

It's under the Apache 2.0 license. The core engine code is available

What's interesting architecturally is they seem to have separated the main logic

Which is mostly C++ for the bits that connect to the web server

Attachment and C and the part that sinks learning data between agents smart sync

and go

that modularity helps with auditing and critically the sources highlight an

independent third-party security audit was done on the code back in

2022 that definitely helps build confidence. Okay, this has been a really

insightful dive

We've traced the shift haven't we from those brittle signature based rules needing

constant tweaking that high maintenance of draft job

Towards something self-learning adaptive focused on preemptive defense

The big takeaway here seems to be about changing the operational overhead of

application security

Moving away from endless manual tuning. Let's security teams. Maybe focus on higher-level

strategy

It really could shift the focus and that leads to a really interesting question for

you the listener to think about

If this kind of technology truly becomes an install and forget solution one that

learns continuously and defends preemptively

What does that mean for the future role of the security analyst?

Particularly the ones whose main job today is that constant monitoring and fine-tuning

of complex security policies

That's the potential strategic shift to consider in your own planning a really

provocative thought to end on think about how that might change things

In your own environment or career. Thank you for joining us for this deep dive

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Join us next time on the deep dive

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